לוח משרות דרושים NLP/Machine Learning משרה מלאה, לחיפוש זה נמצאו 91 משרות

we are looking for a Research Engineer
As a research engineer , you'll be at the forefront of building systems to evaluate and secure frontier AI models. You'll work on infrastructure and experiments to assess model capabilities, implement agent frameworks, and develop mitigations for advanced AI systems. Your role will involve creating robust evaluation pipelines, developing security-focused testing frameworks, and building tools that help understand and mitigate risks related to frontier models. Youll have a chance to understand the research context and your codes impact and contribute as a meaningful part of a growing team.
Representative projects:
Building a tool to continuously evaluate models and mitigate their risks. From designing the APIs for frontier labs, to building analysis and visualization tools that summarize 10,000+ transcripts into specific conclusions.
Designing and building challenges that measure a models ability to evade discovery, allowing us to see if models can operate on remote systems while avoiding detection by common defensive security tools.
Developing controlled environment frameworks for more secure use of frontier models.
Designing and building agents that improve a models ability to complete complex tasks. Includes many potential avenues, such as incorporating SOTA prompting practices, creating tools for task delegation, and more.
Publishing your research and/or delivering research to our customers.

we are looking for a Senior Software Engineer.
As a Software Engineer , you will take ownership of designing, building, and scaling the production systems that power our evaluation and security platform for frontier AI models.
Your work will focus on creating robust, resilient, and high-performance infrastructure-whether thats distributed pipelines, backend services, or tooling that supports our research teams.
This role is engineering-first with a strong research and cyber component. You will develop systems that must run reliably in production, integrate with external partners, and support large-scale data, experiments, and automated evaluations. Youll drive architectural decisions, lead technical implementations, and shape how our platform evolves.
Representative Responsibilities:
Architecting and scaling production-grade systems and workflows.
Building backend services, APIs, and monitoring tools for large-scale model evaluations.
Designing infrastructure that supports research experiments at scale.
Implementing agent frameworks in production environments
Designing and building challenges that measure a models ability to evade discovery, allowing us to see if models can operate on remote systems while avoiding detection by common defensive security tools.

We're looking for a Senior AI Solutions Builder to join our Product team. In this role, you will design, build, and ship AI-powered solutions that enhance productivity and drive measurable business value across the organization.
You will operate at the intersection of AI, data, pricing and business - owning the end-to-end development of agents, copilots, and workflow automations that directly inform decisions and eliminate manual work.
You are both a builder and an enabler. You ship solutions yourself and raise the AI capability floor for the teams around you. You'll collaborate closely with Product, Engineering, CS, Sales, and builders across the company to deliver outcomes that improve decision velocity, product and operational efficiency, and long-term growth.
You Will:
Design and ship AI-powered copilots, agents, and decision-support tools for the Product team, starting with Pricing
Build data pipelines and analytical models for pricing intelligece
Develop autonomous agents that automate recurring Product tasks
Automate manual workflows across product tools so the right action happens when a signal fires
Build LLM-based scoring models that give roadmap and prioritization decisions a data backbone
Enable and upskill product teams through AI playbooks, sessions, and hands-on co-building
Collaborate with builders across Engineering, CS, GTM, and other teams to co-create solutions that scale
Define and track impact: time-to-insight, decision velocity, manual hours eliminated, and business outcomes

The ideal candidate is passionate about data and AI, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment. We value curiosity as much as experience: we are looking for someone eager to show what they know, and equally eager to keep learning in a field that moves fast.

Responsibilities
Explore, analyze, and model large volumes of structured and unstructured data in Python, from exploratory analysis and feature engineering through to model validation and communication of results.
Design, train, evaluate, and deploy machine learning models, and monitor their performance, accuracy, and drift in production.
Build and orchestrate Agentic AI solutions - LLM-based agents, RAG pipelines, prompt design, and evaluation frameworks - to automate data quality checks, investigation, and reporting workflows.
Integrate models and agents with internal systems and data sources using MCP servers and clients, and workflow automation platforms such as n8n.
Write efficient and maintainable SQL queries to support analysis, reporting, and data exploration needs.
Collaborate with data engineers to productionise models and agents: reliable data flows, logging, alerting, and performance tuning.
Participate in the development of internal tools and dashboards that make data and AI capabilities accessible across the organization.
Share findings with the team and help evaluate emerging AI tooling as the ecosystem evolves.

Required Senior ML Data Engineer
About the team:
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

Senior AI Research Engineers at Simply develop algorithmic solutions that power new innovative features and continuously improve existing ones, with a strong emphasis on training AI / ML models as a core part of the role. The work spans a wide range of domains - from training generative AI and LLM-based models to create musical content at scale for Simply Piano, through building deep learning computer vision models that enhance user drawings in Simply Draw, to developing real-time audio ML models for music transcription that elevate feedback.

You will take full end-to-end ownership of cutting-edge algorithms and AI models - leading the process from ideation and research, through development, to production-grade implementation and engineering within products. This includes not only designing and building AI models, but also ensuring robust, efficient, and scalable integration into the app - directly shaping the experience of millions of users.

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning systems that improve the calibration, control, and operation of quantum processors. In this role, you will work at the intersection of machine learning, quantum physics, and software engineering, translating noisy, non-stationary, safety-critical control problems into ML solutions that run on real hardware in production labs.

You will develop reinforcement learning policies, Bayesian inference methods, and agentic frameworks that make quantum control more autonomous, more sample-efficient, and more robust to drift. This position offers unprecedented exposure to diverse qubit types and quantum architectures, with a tight feedback loop between your models and the systems they steer, and the opportunity to deliver groundbreaking ML-driven solutions to the labs and companies defining the next generation of quantum systems.

Responsibilities:

Develop reinforcement learning, Bayesian inference, and probabilistic modelling approaches for parameter tuning, drift tracking, and adaptive measurement, to be deployed on real hardware.
Develop real-time parameter steering for calibration during QEC and between circuits.
Develop and maintain agentic frameworks for autonomous system control and calibration.
Develop and maintain Python-based ML services and libraries that integrate with the wider Quantum Machines control stack, including QUA, Qualibrate, and the OPX1000.
Work directly with customers and partner labs to deploy, validate, and iterate on ML solutions in real experimental environments.
Collaborate cross-functionally with product, R&D, and hardware teams, contributing to internal libraries, customer-facing SDKs, and training materials.

We are looking for a Senior ML Algorithm Engineer to lead the development and optimization of machine learning models for challenging real-world problems.
In this role, you will work hands-on across the full model-development lifecycle: understanding the task, designing and adapting algorithms, building effective training strategies, analyzing data and failure modes, and improving model quality and efficiency through rigorous experimentation.
This is an algorithm-focused role for someone who enjoys getting deeply involved in the details of model training and optimization. You will not simply operate an existing training infrastructure-you will investigate open-ended problems and develop practical solutions involving model architecture, data and sample selection, training objectives, optimization methods, and evaluation
What will your job look like:
Work on the semantics of road objects, using harvested tabular data as the primary input to our algorithms - turning large-scale, real-world observations into models that capture object meaning, attributes, and behavior on the road network.
Design, implement, and optimize machine learning and deep learning algorithms for these semantic tasks, from architecture and training objectives through evaluation and efficiency.
Develop and improve end-to-end model training pipelines, from data preparation and sampling of harvested tabular datasets through training and evaluation.
Investigate model behavior, identify failure modes, and drive targeted algorithmic improvements.
Develop effective strategies for data selection, dataset composition, sampling, augmentation, loss design, and training schedules.
Lead the investigation of complex algorithmic challenges, uncover patterns in data and model behavior, and translate insights into measurable improvements.

Required Outstanding Graduate - Machine Learning Engineer
About the team:
Our Hawkeye team is developing advanced close-range 3D perception for autonomous vehicles, enabling safe automated parking and low-speed maneuvers in tight environments. We build multi-camera deep learning systems for precise and reliable understanding of the vehicles surroundings.
What will your job look like:
Research and develop cutting-edge end-to-end 3D perception models combining geometry and semantics.
Work on 3D Semantic Occupancy, Geometry Reasoning, 3D Reconstruction, 3D Object Detection, and related tasks.
Innovate in multi-view fusion, motion handling, and spatial representation learning.
Deliver models that run on real vehicles and integrate into production systems.

Experienced Machine Learning Engineer
About the team:
Our Hawkeye team is developing advanced close-range 3D perception for autonomous vehicles, enabling safe automated parking and low-speed maneuvers in tight environments. We build multi-camera deep learning systems for precise and reliable understanding of the vehicles surroundings.
What will your job look like:
Research and develop cutting-edge end-to-end 3D perception models combining geometry and semantics.
Work on 3D Semantic Occupancy, Geometry Reasoning, 3D Reconstruction, 3D Object Detection, and related tasks.
Innovate in multi-view fusion, motion handling, and spatial representation learning.
Deliver models that run on real vehicles and integrate into production systems.

we are looking for an AI Tech Lead to join our M&T engineering organization - the team behind a live, high-availability TV platform serving telecom and media companies worldwide at 99.995% SLA on AWS.
Were not looking for someone who talks about AI strategy. Were looking for someone who picks up a real problem, finds where AI creates a step-change, and makes it happen.
This is an individual contributor role reporting directly to the VP R&D. Youll be embedded in M&T but will work closely with engineering teams across R&D - contributing your AI expertise to shared initiatives, building collaborative relationships, and helping move technical work forward together.
If youre the kind of engineer who gets restless when theres a better way and no one is building it yet - this role is for you.

Required Senior ML Data Engineer
About the team:
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

we are looking for a Principal Architect.
You own the end-to-end architecture of the agentic platform: how speech, avatar generation, and the agentic reasoning layer combine into one coherent system that holds up in production, across many tenants, under a real-time latency budget.
This is the widest technical seat in the group. A single user turn crosses speech recognition, retrieval, planning and tool calling, speech synthesis, and avatar rendering, and the experience is only as good as the seams between them. The scope here is the whole path, including the parts we did not build ourselves: externally sourced capabilities, third-party model providers, and customer-owned systems reached through the gateway.
Some of the decisions here shape the platform for a long time: how the speech pipeline is composed, which real-time orchestration and transport framework the platform standardizes on, how new capabilities are absorbed into the target architecture, and where a shared component belongs versus a per-customer one. You define the criteria these decisions are judged against and work closely with the research team that benchmarks the options, so the call rests on evidence. You write the reasoning down, and you stay close enough to the code to know when reality disagrees with the design.
You are hands-on. You prototype to de-risk, you read the traces yourself, and you stay as close to the numbers as to the diagram.

we are looking for a Senior AI Engineer - Exploration & Prototyping.
That is this role. You take an open question, run a focused spike, and come back with numbers and a recommendation. You read the source of the frameworks you evaluate rather than trusting their marketing. You build prototypes to settle arguments.
You do not own a subsystem and you do not ship to customers, which is exactly what protects the work: exploration inside a delivery team always loses to the sprint. You sit alongside the platform, research, and forward-deployed teams, you borrow their context freely, and your output is evidence they can act on.
You will be trusted with real influence early. The recommendations you write become the architecture other people build against, so the bar is not a working demo but a defensible conclusion, including the ones that say no.
What Youll Do:
Run technical spikes that close open decisions, covering agent orchestration frameworks, real-time transport, memory protocols, agent interoperability standards, LLM selection and routing, evaluation harnesses, and the production library and stack choices underneath all of it.
Build prototypes to de-risk, standing up something real quickly, proving or disproving the thing in question, and moving on without becoming attached to the code.
Read and evaluate unfamiliar codebases, going into the source of a candidate framework to find out whether it can actually support what we need rather than what its documentation implies.
Design the measurements that make a decision defensible, building the harness, running the comparison, and reporting latency, cost, and failure behavior honestly.
Own build-versus-adopt recommendations for platform infrastructure, frameworks, and libraries, including a clear statement of what it would cost to be wrong.
Write the recommendation down. Every spike ends in a short, decisive document another engineer can act on, with the evidence, the rejected options, and the reasoning behind the call
Hand off cleanly, transferring what you learned to the team that will own the capability in production, and staying available while they pick it up.
Track the landscape across agentic infrastructure, real-time frameworks, and adjacent AI tooling, and bring forward the things that genuinely change what we can build.

We are seeking a Senior NLP/LLM Researcher to conduct advanced research and develop innovative applications focused on protecting and evaluating LLMs & agentic applications. In this role, you will collaborate with a multidisciplinary team of scientists and engineers, working together to address critical challenges. You will be responsible for designing, implementing, and evaluating new algorithms, models, and agents that help safeguard LLMs.